mollymr305 / mnist-mc-dropout
model uncertainty using mc dropout
☆20Updated 5 years ago
Related projects: ⓘ
- ☆32Updated this week
- Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference, Gal et al. 2015☆35Updated 6 years ago
- contains the code for models in the paper Robust, Deep and Inductive Anomaly Detection☆34Updated 7 years ago
- Implementation of Bayesian NNs in Pytorch (https://arxiv.org/pdf/1703.02910.pdf) (With some help from https://github.com/Riashat/Deep-Ba…☆31Updated 3 years ago
- Code for the paper 'Understanding Measures of Uncertainty for Adversarial Example Detection'☆57Updated 6 years ago
- Implementations of the ICML 2017 paper (with Yarin Gal)☆39Updated 6 years ago
- Repository with code for paper "Inhibited Softmax for Uncertainty Estimation in Neural Networks"☆25Updated 5 years ago
- TensorFlow implementation of Bayes-by-Backprop algorithm from "Weight Uncertainty in Neural Networks" paper☆51Updated 5 years ago
- An implementation of "Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles" (http://arxiv.org/abs/1612.01474)☆34Updated 7 years ago
- My implementation of the paper "Simple and Scalable Predictive Uncertainty estimation using Deep Ensembles"☆134Updated 6 years ago
- Implementation and evaluation of different approaches to get uncertainty in neural networks☆139Updated 6 years ago
- Denoising Adversairal Autoencoders☆40Updated 7 years ago
- Multiplicative Normalizing Flow (MNF) posteriors for variational Bayesian neural networks☆64Updated 4 years ago
- Code for the paper Implicit Weight Uncertainty in Neural Networks☆65Updated 4 years ago
- Code for "Well-calibrated Model Uncertainty with Temperature Scaling for Dropout Variational Inference" (NeurIPS Bayesian Deep Learning W…☆23Updated 4 years ago
- Implementation of the MNIST experiment for Monte Carlo Dropout from http://mlg.eng.cam.ac.uk/yarin/PDFs/NIPS_2015_bayesian_convnets.pdf☆30Updated 4 years ago
- ☆25Updated 2 years ago
- Uncertainty estimation on Mnist dataset☆22Updated 6 years ago
- Bayesian Backprop RNN implementation pytorch https://arxiv.org/abs/1704.02798☆25Updated 6 years ago
- ☆53Updated 6 years ago
- Dropout as Regularization and Bayesian Approximation☆54Updated 5 years ago
- Demos demonstrating the difference between homoscedastic and heteroscedastic regression with dropout uncertainty.☆139Updated 8 years ago
- Code for Deep Bayesian Active Learning (ICML 2017)☆111Updated 6 years ago
- ☆25Updated 5 years ago
- Uncertainty interpretations of the neural network☆31Updated 6 years ago
- Implementation of Ladder Network in PyTorch.☆45Updated 7 years ago
- Learning error bars for neural network predictions☆69Updated 4 years ago
- Epistemic Uncertainty Estimation with Monte Carlo Dropout☆8Updated 4 years ago
- Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors